An interoperable ontology-based information model for better integration of building physics and IoT data analytics models
Bibliographic record
Abstract
Developing ontologies and information models is crucial for structuring knowledge and enhancing interoperability across various fields, particularly in the building and energy data sectors. This article examines the evolution of ontology development methodologies, emphasizing their significance in managing complex data and overcoming interoperability challenges. An interoperable ontology-based information model has been developed to better integrate IoT with building physics analysis model data. This multi-component model is created by defining an overall goal, reusing existing ontologies, and establishing the necessary classes and properties. A specific use case has been implemented in Montreal, Canada, where static building data (related to building physics) from the TOOLS4Cities hub hub [1], [2] and dynamic time-series energy consumption data have been harmonized. This application demonstrates how building energy models can automatically incorporate static data and utilize time-series measured consumption datasets to calibrate simulated energy demands. This approach highlights the potential of ontology-based data models to enhance energy efficiency and sustainability in urban environments, facilitating more informed decision-making and optimizing energy consumption management in buildings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".